There is an ever-increasing need for real-time communication and rendering of volumetric images, especially medical images (e.g., computed tomography (CT), magnetic resonance imaging (MRI), etc.) due to rapidly growing demands of, for example, telemedicine, remote collaboration, and distributed networks. Because of the large data volume associated with such images, high compression is often relied upon not only when storing the image, but also when transmitting the same over a network from one point to another. Such data compression (known as “lossy” compression), however, is disadvantageous in that a loss of an image's original fidelity often results. Yet, due to the seriousness of medical diagnostics and procedures, “lossless” reconstruction of an image is, on many occasions, imperative as small image details can influence the detection of pathology and alter a given diagnosis.
One approach to address these competing requirements (lossy vs. lossless compression), is to compress medical images in a scalable and/or object-based fashion, allowing a volumetric image to be rendered, in a first instance, relatively quickly with a lossy reconstruction and then, either automatically or upon command, to be rendered with increased refinement to a point where reconstruction is considered lossless. By limiting or prioritizing the refinement to selected or more important regions, transmission efficiency can be further achieved. Several research efforts in the literature address compression and transmission technologies that support fast visualization and subsequent/selective quality refinement.